Abstract
The electrocardiogram (ECG) signal is made up of sequences of three distinct waves, including the P-wave, QRS-complex, and T-wave. These sequences may contain several different varieties of feature representation, which means various appearances represent different attribute knowledge of heart activity. Consequently, delicate analysis of the ECG attribute knowledge is critical, which focuses on empowering computers to understand ECG signals in a way physicians do. Traditional ECG systems usually only focus on the key wave detection or sub-sequences morphological classification, and the complex ECG features make the analysis process difficult to handle both tasks. In this paper, our goal is to propose a novel ECG analysis technique for ECG medical attribute knowledge feature extraction, termed ECG-MAKE method. The ECG-MAKE method mainly addresses two challenging issues—ECG signal key points detection and morphological delineation—by introducing four modules: signal preprocessing, key point detection, adaptive error correction, and attribute knowledge extraction. Signal preprocessing is based on a wavelet multi-resolution algorithm, which extracts both time and frequency domain information simultaneously, and achieves the goal of enhancing raw signals by filtering signals below 1 Hz and above 100 Hz. The key point detection combines mean-value difference and sliding window to detect R-peak. And according to the R-peak, further utilizes the ECG rules and threshold to detect the onset, peak, and offset of different heartbeat waveforms such as the P-waves and T-waves. Then, for standard 12-lead ECG, the adaptive error correction can automatically correct the spatial position of certain leads missed and false detection key points. Finally, the attribute knowledge extraction module is based on the statistical features of key points to obtain the ECG attributes knowledge of clinical diagnosis of ECG. Extensive experiments conducted on the publicly available database QT database (99.57% F1-score) have demonstrated the effectiveness of the proposed ECG-MAKE method. The proposed method has a higher classification performance than current state-of-the-art methods with an F1-score performance of 94.30% on the ZZU-ECG database.
•A medical attribute knowledge extraction method is proposed to delineate ECG signal.•A strategy is proposed to correct missed and false detections of ECG knowledge.•A framework is proposed to provide ECG refined annotation for deep learning model.•High classification performance compared to current state-of-the-art methods.